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1.
参考作物蒸发蒸腾量(ET0)是计算作物需水量的基础,一般用FAO推荐的Penman-Monteith公式(PM公式)计算。但是在河套灌区部分地区缺少辐射数据的观测,因而无法利用PM公式计算ET0。本文选用河套灌区临河气象站1990—2012年的气象资料,分析了利用PM公式计算参考作物蒸发蒸腾量ET0与气象要素的关系,发现对ET0影响最大的气象因素为净辐射,其次为饱和水气压差和平均温度。建立了基于饱和水气压差、温度和风速的ET0估算公式,验证结算显示相关系数、纳什效率系数和总量平衡系数分别为0.96、0.92、1.00。在风速缺测的条件下,也建立了基于饱和水汽压差和温度的ET0估算公式。以上两个公式为河套灌区缺资料条件下ET0的估算提供了简单且准确的估算方法。  相似文献   

2.
小麦禾谷缢管蚜的危害损失和防治指标研究   总被引:8,自引:0,他引:8  
禾谷缢管蚜种群数量和危害历期是造成小麦产量损失的主要因素。采用累积虫日作为危害量指标 ,建立了蚜虫危害量与小麦产量损失的回归模型 ,即Y1=1.4250+5.3529×10-4X1,Y2=1.1780+0.0106X2 ,确定了禾谷缢管蚜的动态防治指标  相似文献   

3.
采用相关分析和通径分析法研究了稻水蝇危害与水稻产量损失的关系。结果表明:水稻产量(y,kg/hm2)与田间虫口密度(x,头 /m2)、穗损失率 (x5,% )间0.01水平显著时的关系符合方程:y^=9433.965-6.6637x1-402.7469x5;产量损失(Y,kg/hm2)与田间虫口密度(x,头/m2)间关系符合下列方程 :Y^=-13.4989+6.0043x(r=0.9647**)。通径分析显示 ,穗损失率和虫口密度对产量建成直接效应最大 ,分别为-0.9218和-0.1422  相似文献   

4.
试验表明,水稻孕穗期每丛水稻茎毛眼水蝇卵数()、被害穗数()与产量损失()的关系符合下列方程:=-2.01+4.524±2.2,=0.9914;=-0.076+4.912±2.78,=0.9863。产量损失的主导因素是水稻受害后,早稻实粒数减少,晚稻总粒数和实粒数减少。根据防治费用,稻谷价格等,导出孕穗期稻茎毛眼水蝇的经济阈值为每丛禾1.9粒卵或1.34株受害穗。经大田验证,与实际基本相符。  相似文献   

5.
为探寻不同施氮量对农田土壤呼吸(RS)的影响并快速准确估算RS,以关中地区冬小麦为研究对象,观测了5种施氮量下冬小麦农田RS的变化,研究了环境因子(土壤温度、土壤湿度)及作物因素(叶面积指数、地上部生物量、SPAD值)对于RS的影响,建立了适用于关中地区土壤温度与植被指数下的农田土壤呼吸估算模型。设置秸秆还田下的5种施氮量处理,分别为传统施氮量SN200(200 kg·hm-2)、优化施氮量SN150(150 kg·hm-2)、60%优化施氮量SN120(120 kg·hm-2)、50%优化施氮量SN100(100 kg·hm-2)以及不施氮肥SN0(0 kg·hm-2)。结果表明:不同施氮量下RS随生育期推进均表现为先升高再降低的趋势,同时添加氮肥促进了RS排放。各处理观测期内RS的均值为:SN200(3.68 μmol·m-2·s-1)>SN150(3.40 μmol·m-2·s-1)>SN120(3.06 μmol·m-2·s-1)>SN100(2.70 μmol·m-2·s-1)>SN0(2.21 μmol·m-2·s-1)。不同施氮量下冬小麦冠层近红外波段反射率在拔节期和抽穗期差异明显,反射率从高到低依次为SN200>SN150>SN120>SN100>SN0,而在灌浆期和成熟期差异不大。土壤温度显著影响了RSP<0.01),土壤湿度与RS没有显著相关关系(P0.05)。叶面积指数、地上部生物量、SPAD值和植被指数均与RS呈显著相关关系(P<0.05)。通过多种模型评估,建立基于植被指数和土壤温度的最佳农田土壤呼吸估算模型,显著高于基于土壤温度的单因子模型,模型精度可达到0.6以上(n=120)。  相似文献   

6.
为探索定量评估干旱灾害对旱区冬小麦造成损失的方法,以山东省莒县为例,利用1981—2010年莒县三十年气候整编资料以及历年冬小麦生长发育期、土壤水分观测资料,运用FAO PM公式,对气象行业标准《小麦干旱灾害等级》中冬小麦不同生长发育阶段的可能蒸散量、需水量、水分亏缺率进行求算,对小麦不同生育阶段作物系数表(Kc)中后期阶段的发育期、Kc的界定取值以及有关计算公式等进行订正应用研究。结果表明:小麦生育期间各生育阶段总可能蒸散量518.741 mm、不同作物系数小麦总需水量466.393 mm,平均作物系数总需水量440.93 mm,进而求算的阶段水分亏缺率符合实际,效果良好,用水分亏缺率作为评估干旱对冬小麦造成损失的方法可取,可以满足依据水分亏缺率对小麦产量的预报评估需要,可为基层和各级科研人员掌握和了解小麦干旱灾害评估方法提供切实可行的参照依据。  相似文献   

7.
接虫试验表明,水稻苗期每丛稻茎毛眼水蝇卵数()、为害株数()与产量损失()的关系符合下列方程:=-7.31+3.016x1±2.15,=0.9804;=-6.7+3.53x2±2.19,=0.9802。早稻产量损失的主要原因是受害后千粒重和实粒数减少;晚稻为千粒重下降。根据当前的稻谷价格、防治费用等,导出水稻苗期稻茎毛眼蝇的经济阈值为每丛4.6粒卵或3.74株受害。经大田验证,与实际基本相符  相似文献   

8.
为准确估算半湿润地区葡萄园蒸发蒸腾量,在测定气象数据的基础上,以水量平衡法的实测蒸发蒸腾量(ET)为参考,分析判断波文比-能量平衡法估算半湿润地区葡萄园蒸发蒸腾量的适用性以及整个生育期内葡萄ET的变化规律,分别采用单作物系数法(Kc)、双作物系数法(Kcb)估算半干旱半湿润地区葡萄ET。结果表明:全生育期内波文比-能量平衡法与水量平衡法之间的均方根误差(RMSE)与纳什系数(NSE)分别为0.54与0.64,决定系数为0.82,说明波文比-能量平衡法可以较好地应用于半湿润地区葡萄园蒸发蒸腾估算,双作物系数法比单作物系数法估算结果更为精确,计算出的双作物系数0.85、1.07、0.71可以作为本地区值。  相似文献   

9.
黄河源区近40年参考作物蒸散量变化特征研究   总被引:6,自引:0,他引:6       下载免费PDF全文
选取位于黄河源区10个气象台站1971—2010年观测资料,运用彭曼—蒙蒂斯公式计算出各站参考作物蒸散量(ET0)。通过数学统计、相关分析、小波分析等方法对黄河源区ET0分别作了空间分布、年内变化和年变化等特征分析,结果发现源区ET0空间分布不均匀,呈现西北部大于东南部。年内ET0逐月变化表现为典型的单峰型;源区ET0的四季分布差异较大,夏季蒸散量最大,冬季最小,春、秋季次之。各季节ET0与气温和日照呈显著正相关,而与降水量和相对湿度呈明显负相关。ET0年际变化为逐年波动式上升趋势,整个源区年平均ET0以6.1 mm·10a-1的气候倾向率逐年增大。40 a间ET0曾出现过两次较为明显的准周期变化,分别在20世纪70年代中期至80年代中期,约为准8 a周期,1990年以后基本表现为准5 a周期变化。  相似文献   

10.
在舟曲沙滩林场设立固定标准地,进行云杉落针病病菌孢子捕捉和病情调查,用回归预测法对病害进行预测。结果表明:病害流行与降雨、气温关系密切。以前1年4~7月月平均气温(X1)、月平均降雨量(X2)、月平均相对湿度(X3)、温雨比(X4)、温湿比(X5)为自变因子,用多元线性回归建立预测模型。模型预测准确、精度高。  相似文献   

11.
Modelling the effect of crop and weed on herbicide efficacy in wheat   总被引:1,自引:0,他引:1  
BRAIN  WILSON  WRIGHT  SEAVERS  & CASELEY 《Weed Research》1999,39(1):21-35
Recommended field application rates of herbicides have to give effective weed control in every situation and are, thus, often higher than that required for specific fields. An understanding of the interaction between crop:weed competition and herbicide dose may, in many cases, allow herbicide application rates to be reduced, important both environmentally and economically. We have developed a model of the interaction between crop:weed competition and herbicide dose, using an empirical model of the relationship between crop yield and weed biomass (related to weed density), and an empirical model of the relationship between weed biomass and herbicide dose. The combined model predicts crop yield, given herbicide dose and weed biomass at an interim assessment date. These crop yield loss predictions may be used to quantify the herbicide dose required to restrict yield loss to a given percentage. Parameters of the model were estimated and the model tested, using results from experiments, which used cultivated oats ( Avena sativa ) or oilseed rape ( Brassica napus ) as model weeds in a crop of winter wheat ( Triticum aestivum ).For the crop:weed:herbicide combinations investigated there was little increase in crop yield for herbicide dose rates above 20% of recommended field rates, in broad agreement with the model predictions. There may still be potential for further reduction below this level on economic grounds; the model could be used to estimate the `break-even' herbicide dose.  相似文献   

12.
The effects of a range of herbicide doses on crop:weed competition were investigated by measuring crop yield and weed seed production. Weed competitivity of wheat was greater in cv. Spark than in cv. Avalon, and decreased with increasing herbicide dose, being well described by the standard dose–response curve. A combined model was then developed by incorporating the standard dose–response curve into the rectangular hyperbola competition model to describe the effects of plant density of a model weed, Brassica napus L., and a herbicide, metsulfuron‐methyl, on crop yield and weed seed production. The model developed in this study was used to describe crop yield and weed seed production, and to estimate the herbicide dose required to restrict crop yield loss caused by weeds and weed seed production to an acceptable level. At the acceptable yield loss of 5% and the weed density of 200 B. napus plants m–2, the model recommends 0.9 g a.i. metsulfuron‐methyl ha–1 in Avalon and 2.0 g a.i. in Spark.  相似文献   

13.
Modelling the effects of weeds on crop production   总被引:3,自引:0,他引:3  
M. J. KROPFF 《Weed Research》1988,28(6):465-471
In most quantitative studies on interplant competition, static regression models are used to describe experimental data. However, the generality of these models is limited. More mechanistic models for interplant competition, which simulate growth and production of species in mixtures on the basis of the underlying physiological processes, have been developed in the past decade. Recently, simulation models for competition between species for light and water were improved and a detailed version was developed for sugarbeet and fat hen (Chenopodium album L.). The model was validated with data sets of five field experiments, in which the effect of fat hen on sugarbeet production was analysed. About 98% of the variation in yield loss between the experiments (which ranged from –6 to 96%) could be explained with the model. Further analysis with the model showed that the period between crop and weed emergence was the main factor causing differences in yield loss between the experiments. Sensitivity analysis showed a strong interaction between the effect of the variables weed density and the period between crop and weed emergence on yield reduction. Different quantitative approaches to crop-weed competition are discussed in view of their practical applicability. Simulations of experiments, where both the weed density and the period between crop and weed emergence were varied over a wide range, showed a close relation between relative leaf cover of the weeds shortly after crop emergence and yield loss. This relation indicates that relative leaf cover of the weeds accounts for both the effect of weed density and the period between crop and weed emergence. This relation has the potential to be developed into a powerful tool for weed-control advisory systems.  相似文献   

14.
High weed abundance in organic crops is thought to be a key factor contributing to the greater yield loss in organic as compared with conventional cropping systems. However, even with greater weed densities than conventional systems, some organic systems have yields comparable to conventional systems, suggesting that cropping systems might differ in yield loss due to weed competition. The diversity in soil nutrient resources due to diversity in crop rotations and variable inputs might enhance crop tolerance to weed competition. We assessed the long‐term effects of contrasting levels of crop rotations (low, medium and high diversity) on weed density, weed biomass and wheat yield loss in organic and no‐till conventional cropping systems using a microplot study within a long‐term cropping systems trial at Scott, Saskatchewan, Canada. Weed density and biomass were found to be four times higher in the organic systems than in the conventional systems. Under standard weed management practices, organic had 44% lower yield than the conventional system. Lower yields in organic, even without weed competition, suggest that the lower yields are due to low soil productivity rather than weed competition. No differences in yield loss were observed among the organic and conventional systems or among the diverse crop rotations. We conclude that the organic management practices and/or increased crop rotation diversity did not enhance yield or reduce yield loss due to weed competition, due to the factors associated with lower soil fertility.  相似文献   

15.
夏玉米地杂草为害的产量损失模型   总被引:1,自引:0,他引:1  
为了明确杂草在对杂草密度与夏玉米产量损失之间的函数关系的影响及反枝苋和马齿苋为害夏玉主的产理损失模型,在田间开展了此项研究。试验结果表明,反枝苋和马齿苋为害均不显著地影响夏玉米的籽粒得和单位面积穗数,造成夏玉米产量损失主要是通过降低每穗的籽粒数要草密度和夏玉米产量损失之间的函数关系因杂草种类不同而异,描述反枝苋密度和夏玉米产量损失之间的关系用双曲线优于S形曲线,而描述马齿苋密度和夏玉米产量损失之间  相似文献   

16.
A new simple empirical model for early prediction of crop losses by weed competition was introduced. This model relates yield loss to relative leaf area of the weeds shortly after crop emergence using the relative damage coefficient q as the single model parameter. The model is derived from the hyperbolic yield density relationship and therefore accounts for the effects of weed density. It is shown that the model also accounts for the effect of different relative times of weed emergence. A strong advantage of the approach is that it can be used when weeds emerge in separate flushes. The regression model described experimental data on sugar-beet – lambsquarters (Beta vulgaris L. –Chenopodium album L.) and maize-barnyard grass (Zea mays L. –Echinochloa crus-galli L.) competition precisely. The model describes a single relationship between crop yield loss and relative leaf area of the weeds over a wide range of weed densities and relative times of weed emergence. Possibilities for scientific and practical application of the model are discussed.  相似文献   

17.
The effects of sub‐lethal dose of herbicide and nitrogen fertilizer on crop–weed competition were investigated. Biomass increases of winter wheat and a model weed, Brassica napus, at no‐herbicide treatment with increasing nitrogen were successfully described by the inverse quadratic model and the linear model respectively. Increases in weed competitivity (β0) of the rectangular hyperbola and parameter B in the dose–response curve for weed biomass, with increasing nitrogen were also successfully described by the exponential model. New models were developed by incorporating inverse quadratic and exponential models into the combined rectangular hyperbola with the standard dose–response curve for winter wheat biomass yield and the combined standard dose—response model with the rectangular hyperbola for weed biomass, to describe the complex effects of herbicide and nitrogen on crop–weed competition. The models developed were used to predict crop yield and weed biomass and to estimate the herbicide doses required to restrict crop yield loss caused by weeds and weed biomass production to an acceptable level at a range of nitrogen levels. The model for crop yield was further modified to estimate the herbicide dose and nitrogen level to achieve a target crop biomass yield. For the target crop biomass yield of 1200 g m?2 with an infestation of 100 B. napus plants m?2, the model recommended various options for nitrogen and herbicide combinations: 140 and 2.9, 180 and 0.9 and 360 kg ha?1 and 1.7 g a.i. ha?1 of nitrogen and metsulfuron‐methyl respectively.  相似文献   

18.
The algorithm of an optical detection system was first investigated for its ability to correctly classify transplanted crops and weeds during the critical early stages of crop establishment and its robustness over a range of different crop species. The trade-off was then examined between increasing the sensitivity of the detection system vs. the possibility of, in doing so, misclassifying some crop plants as weeds and inadvertently removing them. This was achieved by running a competition model using parameters derived from the image analysis and assessing the outcome of scenarios in terms of yield. The optimum parameter values to maximize the detection of the crop and the optimum parameter values to maximize the detection of the weed appeared relatively insensitive to time of image capture or weed density. They also appeared insensitive for different crop species where the crop had similar growth habit. However, competition scenarios indicated that the detection system parameter settings to achieve optimum yields were sensitive to the competitive ability of the weed species. For Veronica persica, crop yield was more sensitive to accidental crop removal than from competition. In contrast, in the presence of Tripleurospermum inodorum, yield loss was more attributable to weed competition. Importantly, linking the detection system with the competition model illustrated the principle that optimum yield may not necessarily be obtained by maximizing weed removal or minimizing crop removal. This first example of combining a detection system with a competition model presents a new opportunity to quantify the sensitivity of image classification in terms of yield.  相似文献   

19.
Losses of crop yield due to weed competition in unweeded plots averaged nearly 60% of weed-free yields in cotton and 70% in groundnuts. Weed competition was not directly related to weed groundcover but was dependent on the seasonal growing conditions. The critical period of weed competition in both crops was the 6 weeks between 4 and 10 weeks after crop emergence. During this period cotton could tolerate up to 25% weed groundcover without appreciable loss in crop yield. Groundnuts could tolerate not more than 10% weed cover before yield loss occurred. A main factor in achieving standards of weed control within these limits was preventing the early growth of monocotyledonous weed species: pre-sowing application of trifluralin and benfiuralin provided this over a wide range of growing conditions.  相似文献   

20.
Lutman  Bowerman  Palmer  Whytock 《Weed Research》2000,40(3):255-269
Ten experiments have investigated competition between winter oilseed rape and Stellaria media (common chickweed). Yield losses caused by this weed were often high, but differed greatly between experiments, 5% yield loss being calculated to be caused by 1.4–328 plants m?2. Predictions of yield loss based on relative weed dry weights [weed dry weights/(crop + weed dry weights)] in December were somewhat less variable than those based on weed density, 5% yield loss being caused by 1.4–10.6% relative weed dry weight. The variations in yield loss were related to variations in the competitiveness of the oilseed rape and the S. media, caused by weather differences between years and sites, and the long period between weed assessment and harvest (8–10 months). However, despite the lack of precise relationships, there were indications that the greater the crop dry weights in December, the lower the final yield loss. Delayed sowing of oilseed rape until late September did not clearly increase the competitive effects of the weed compared with late August/early September sowings. Weed competition was not clearly affected by reduced crop density (44–113 plants m?2), because of the compensatory ability of the lowest density. The results of the experiments are discussed in relation to the prediction of yield loss and, thus, possible adjustment of weed control strategies to meet expected crop losses.  相似文献   

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